# STAT2

## First EditionNew Edition Available Ann R. Cannon; George W. Cobb; Bradley A. Hartlaub; Julie M. Legler; Robin H. Lock; Thomas L. Moore; Allan J. Rossman; Jeffrey A. Witmer

©2014*offers students who have taken AP Statistics or a typical introductory statistics college level course to learn more sophisticated concepts and the tools with which to apply them.*

**STAT2***students should be able to:*

**STAT2**1. Choose the appropriate statistical model for a particular problem.

2. Know the conditions that are typically required when fitting various models.

3. Assess whether or not the conditions for a particular model are reasonably met for a specific dataset. 4. Have some strategies for dealing with data when the conditions for a standard model are not met.

5. Use the appropriate model to make appropriate inferences.

## Table of Contents

**0 What Is a Statistical Model? **0.1 Fundamental Terminology

0.2 Four-Step Process

0.3 Chapter Summary

0.4 Exercises

**Unit A: Linear Regression**

**1 Simple Linear Regression **1.1 The Simple Linear Regression Model

1.2 Conditions for a Simple Linear Model

1.3 Assessing Conditions

1.4 Transformations

1.5 Outliers & Influential Points

1.6 Chapter Summary

1.7 Exercises

**2 Inference for Simple Linear Regression **2.1 Inference for Regression Slope

2.2 Partitioning Variability - ANOVA

2.3 Regression and Correlation

2.4 Intervals for Predictions

2.5 Chapter Summary

2.6 Exercises

**3 Multiple Regression **3.1 Multiple Linear Regression Model

3.2 Assessing a Multiple Regression Model

3.3 Comparing Two Regression Lines

3.4 New Predictors from Old

3.5 Correlated Predictors

3.6 Testing Subsets of Predictors

3.7 Case Study: Predicting in Retail Clothing

3.8 Chapter Summary

3.9 Exercises

**4 Additional Topics in Regression **4.1 Topic: Added Variable Plots

4.2 Topic: Techniques for Choosing Predictors

4.3 Topic: Identifying Unusual Points in Regression

4.4 Topic: Coding Categorical Predictors

4.5 Topic: Randomization Test for a Relationship

4.6 Topic: Bootstrap for Regression

4.7 Exercises

**Unit B: Analysis of Variance**

**5 One-way ANOVA **5.1 The One-way Model: Comparing Groups

5.2 Assessing and Using the Model

5.3 Scope of Inference

5.4 Fisher’s Least Significant Difference

5.5 Chapter Summary

5.6 Exercises

**6 Multifactor ANOVA **6.1 The Two-way Additive Model (Main Effects Model)

6.2 Interaction in the Two-way Model

6.3 The Two-way Non-additive Model (Two-Way ANOVA with Interaction)

6.4 Case Study

6.5 Chapter Summary

6.6 Exercises

**7 Additional Topics in Analysis of Variance **7.1 Topic: Levene’s Test for Homogeneity of Variances

7.2 Topic: Multiple Tests

7.3 Topic: Comparisons and Contrasts

7.4 Topic: Nonparametric Statistics

7.5 Topic: ANOVA and Regression with Indicators

7.6 Topic: Analysis of Covariance

7.7 Exercises

**8 Overview of Experimental Design**8.1 Comparisons and Randomization

8.2 Randomization F Test

8.3 Design Strategy: Blocking

8.4 Design Strategy: Factorial Crossing

8.5 Chapter Summary

8.6 Exercises

**Unit C: Logistic Regression**

**9 Logistic Regression **9.1 Choosing a Logistic Regression Model

9.2 Logistic regression and odds ratios

9.3 Assessing the logistic regression model

9.4 Formal inference: tests and intervals

9.5 Summary

9.6 Exercises

**10 Multiple Logistic Regression **10.1 Overview

10.2 Choosing, fitting, and interpreting models

10.3 Checking conditions

10.4 Formal inference: tests and intervals

10.5 Case Study: Bird Nests

10.6 Summary

10.7 Exercises

**11 Additional Topics in Logistic Regression **11.1 Topic: Fitting the logistic regression model

11.2 Topic: Assessing Logistic Regression Models

11.3 Randomization Tests

11.4 Analyzing Two-way Tables with Logistic Regression

11.5 Exercises